{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file_name = 'lstm_bias_reg'\n",
    "df = pd.read_csv('result/'+file_name'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>l1 0.0000,l2 0.0000</th>\n",
       "      <th>l1 0.1000,l2 0.0000</th>\n",
       "      <th>l1 0.0100,l2 0.0000</th>\n",
       "      <th>l1 0.0010,l2 0.0000</th>\n",
       "      <th>l1 0.0001,l2 0.0000</th>\n",
       "      <th>l1 0.0000,l2 0.1000</th>\n",
       "      <th>l1 0.0000,l2 0.0100</th>\n",
       "      <th>l1 0.0000,l2 0.0010</th>\n",
       "      <th>l1 0.0000,l2 0.0001</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167918.264453</td>\n",
       "      <td>8.309034e+05</td>\n",
       "      <td>7.745787e+05</td>\n",
       "      <td>1.076617e+06</td>\n",
       "      <td>2.034548e+05</td>\n",
       "      <td>5.296424e+05</td>\n",
       "      <td>134708.910336</td>\n",
       "      <td>212683.333081</td>\n",
       "      <td>172255.474670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156361.292084</td>\n",
       "      <td>5.479787e+05</td>\n",
       "      <td>6.768677e+05</td>\n",
       "      <td>1.000704e+06</td>\n",
       "      <td>2.574953e+05</td>\n",
       "      <td>5.386370e+05</td>\n",
       "      <td>111880.381133</td>\n",
       "      <td>177633.224655</td>\n",
       "      <td>153915.469043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18804.548420</td>\n",
       "      <td>8.547514e+04</td>\n",
       "      <td>7.206564e+04</td>\n",
       "      <td>6.180446e+04</td>\n",
       "      <td>2.136791e+04</td>\n",
       "      <td>3.797214e+04</td>\n",
       "      <td>16444.083344</td>\n",
       "      <td>17603.299222</td>\n",
       "      <td>18352.846544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41748.826708</td>\n",
       "      <td>5.028204e+05</td>\n",
       "      <td>3.279948e+05</td>\n",
       "      <td>3.188271e+05</td>\n",
       "      <td>6.317049e+04</td>\n",
       "      <td>1.738134e+05</td>\n",
       "      <td>45376.262014</td>\n",
       "      <td>106062.758873</td>\n",
       "      <td>43321.819699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82185.084828</td>\n",
       "      <td>7.000922e+05</td>\n",
       "      <td>5.608111e+05</td>\n",
       "      <td>9.104951e+05</td>\n",
       "      <td>1.033341e+05</td>\n",
       "      <td>3.672796e+05</td>\n",
       "      <td>104880.252881</td>\n",
       "      <td>170731.012129</td>\n",
       "      <td>145551.202742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>242849.558510</td>\n",
       "      <td>1.077716e+06</td>\n",
       "      <td>9.126331e+05</td>\n",
       "      <td>1.571230e+06</td>\n",
       "      <td>2.159478e+05</td>\n",
       "      <td>6.304381e+05</td>\n",
       "      <td>160769.244699</td>\n",
       "      <td>259085.574180</td>\n",
       "      <td>226072.170517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>578515.171917</td>\n",
       "      <td>2.127222e+06</td>\n",
       "      <td>2.723278e+06</td>\n",
       "      <td>4.809705e+06</td>\n",
       "      <td>1.117336e+06</td>\n",
       "      <td>2.138945e+06</td>\n",
       "      <td>436508.288788</td>\n",
       "      <td>729647.847248</td>\n",
       "      <td>575573.811153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            l1 0.0000,l2 0.0000  l1 0.1000,l2 0.0000  l1 0.0100,l2 0.0000  \\\n",
       "Unnamed: 0                                                                  \n",
       "count                 30.000000         3.000000e+01         3.000000e+01   \n",
       "mean              167918.264453         8.309034e+05         7.745787e+05   \n",
       "std               156361.292084         5.479787e+05         6.768677e+05   \n",
       "min                18804.548420         8.547514e+04         7.206564e+04   \n",
       "25%                41748.826708         5.028204e+05         3.279948e+05   \n",
       "50%                82185.084828         7.000922e+05         5.608111e+05   \n",
       "75%               242849.558510         1.077716e+06         9.126331e+05   \n",
       "max               578515.171917         2.127222e+06         2.723278e+06   \n",
       "\n",
       "            l1 0.0010,l2 0.0000  l1 0.0001,l2 0.0000  l1 0.0000,l2 0.1000  \\\n",
       "Unnamed: 0                                                                  \n",
       "count              3.000000e+01         3.000000e+01         3.000000e+01   \n",
       "mean               1.076617e+06         2.034548e+05         5.296424e+05   \n",
       "std                1.000704e+06         2.574953e+05         5.386370e+05   \n",
       "min                6.180446e+04         2.136791e+04         3.797214e+04   \n",
       "25%                3.188271e+05         6.317049e+04         1.738134e+05   \n",
       "50%                9.104951e+05         1.033341e+05         3.672796e+05   \n",
       "75%                1.571230e+06         2.159478e+05         6.304381e+05   \n",
       "max                4.809705e+06         1.117336e+06         2.138945e+06   \n",
       "\n",
       "            l1 0.0000,l2 0.0100  l1 0.0000,l2 0.0010  l1 0.0000,l2 0.0001  \n",
       "Unnamed: 0                                                                 \n",
       "count                 30.000000            30.000000            30.000000  \n",
       "mean              134708.910336        212683.333081        172255.474670  \n",
       "std               111880.381133        177633.224655        153915.469043  \n",
       "min                16444.083344         17603.299222         18352.846544  \n",
       "25%                45376.262014        106062.758873         43321.819699  \n",
       "50%               104880.252881        170731.012129        145551.202742  \n",
       "75%               160769.244699        259085.574180        226072.170517  \n",
       "max               436508.288788        729647.847248        575573.811153  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(df.columns[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b494e2d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h63ezezjJJ0opc0n+OMkHuu5PJXlPkvNJ/jzJTyVJrfXFUsrPJ/l81+/naq1X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\nAAAAAKhKAQoAAACAqr4AuDI2eSl7o8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b492908d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,10))\n",
    "df.boxplot()\n",
    "plt.savfig('result/'file_name+'.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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